Abstract [en]

Uncertain and imprecise data are inherent to many domains, e.g. casting lightweight components. Fuzzy logic offers a way to handle such data, which makes it possible to create predictive models even with small and imprecise data sets. Modelling of cast components under fatigue load leads to understanding of material behaviour on component level. Such understanding is important for the design for minimum warranty risk and maximum weight reduction of lightweight cast components. This paper contributes with a fuzzy logic-based approach to model fatigue-related mechanical properties of as-cast components, which has not been fully addressed by the current research. Two fuzzy logic models are constructed to map yield strength to the chemical composition and the rate of solidification of castings for two A356 alloys. Artificial neural networks are created for the same data sets and then compared to the fuzzy logic approach. The comparison shows that although the neural networks yield similar prediction accuracy, they are less suitable for the domain because they are opaque models. The prediction errors exhibited by the fuzzy logic models are 3.53% for the model and 3.19% for the second, which is the same error level as reported in related work. An examination of prediction errors indicated that these are affected by parameters of the membership functions of the fuzzy logic model.

Abstract [en]

In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.

Abstract [en]

Testing of a software system is resource-consuming activity. One of the promising ways to improve the efficiency of the software testing process is to use ontologies for testing. This paper presents an approach to test case generation based on the use of an ontology and inference rules. The ontology represents requirements from a software requirements specification, and additional knowledge about components of the software system under development. The inference rules describe strategies for deriving test cases from the ontology. The inference rules are constructed based on the examination of the existing test documentation and acquisition of knowledge from experienced software testers. The inference rules are implemented in Prolog and applied to the ontology that is translated from OWL functional-style syntax to Prolog syntax. The first experiments with the implementation showed that it was possible to generate test cases with the same level of detail as the existing, manually produced, test cases.

Abstract [en]

The work presented in this paper demonstrates an evaluation procedure for a real-life application ontology, coming from the avionics domain. The focus of the evaluation has specifically been on three ontology quality features, namely usability, correctness and applicability. In the paper, the properties of the three features are explained in the context of the application domain, the methods and tools used for the evaluation of the features are presented, and the evaluation results are presented and discussed. The results indicate that the three quality features are significant in the evaluation of our application ontology, that the proposed methods and tools allow for the evaluation of the three quality features and that the inherent quality of the application ontology can be confirmed.

Abstract [en]

This paper presents an ontology which has been developed to represent the requirements of a software component pertaining to an embedded system in the avionics industry. The ontology was built based on the software requirements documents and was used to support advanced methods in the subsequent stages of the software development process. In this paper it is described theprocess that was used to build the ontology. Two pertinent quality measures that were applied to the ontology, i.e. usability and applicability, are also described, as well as the methods used to evaluate the quality measures and the result of these evaluations.

Abstract [en]

Today XML is a common format supporting interoperabilityand information exchange between systems in the modeling and simulationeld. Although XML enables systems to agree on a common syntaxand understand the exchanged information, systems can misinterpretthem due to their dierent conceptualizations of the domain of interest.In this paper, we present a framework for automatic translation ofXML simulation models which follow the High Level Architecture (HLA) object model template specication, into OWL ontologies. In OWL ontologiesthe semantics of information is formally dened. It provides thebasis for interoperability and information exchange between simulationsystems on semantic level.

Abstract [en]

Workplace innovation (WI) is important to provide betterwork opportunities and increase productivity. WI at the individual tasklevel concerns the structure of individual work tasks. A number of surveyshave been done that measured WI at the individual task level, howeverthey paid little attention to work environment, in particular to supportivetechnology. This paper presents the case study of WI in two Swedishorganisations with focus on the alignment of ICT and the individual worktasks. We carried out seven interviews of workers at dierent levels ofjob and in dierent sectors. The qualitative data analysis identied fourthemes: business processes, working roles, data sources, and technology.The analysis was facilitated by constructing BPMN (Business ProcessModel Notation) diagrams for the identied business processes. We discoveredthat the supportive technology in the organisations is adequatebut downright traditional. We argue that technology is an important factorand enabler for WI. Finally, we present an architectural model thatprovides a direction for future work on WI taking ICT as the basis.

Abstract [en]

Semantic Role Labeling (SRL) plays an important role in different text mining tasks. The development of SRL systems for the biomedical area is frustrated by the lack of large-scale domain specific corpora that are annotated with semantic roles. In our previous work, we proposed a method for building FramenNet-like corpus for the area using domain knowledge provided by ontologies. In this paper, we present a framework for supporting the method and the system which we developed based on the framework. In the system we have developed the algorithms for selecting appropriate concepts to be translated into semantic frames, for capturing the information that describes frames from ontology terms, and for collecting example sentence using ontological knowledge.

Abstract [en]

Semantic Role Labeling plays a key role in many text mining applications. The development of SRL systems for the biomedical domain is frustrated by the lack of large domain specific corpora that are labeled with semantic roles. In this paper we proposed a method for building corpus that are labeled with semantic roles for the domain of biomedicine. The method is based on the theory of frame semantics, and uses domain knowledge provided by ontologies. By using the method, we have built a corpus for transport events strictly following the domain knowledge provided by GO biological process ontology. We compared one of our frames to a BioFrameNet frame. We also examined the gaps between the semantic classification of the target words in this domain-specific corpus and in FrameNet and PropBank/VerbNet data. The successful corpus construction demonstrates that ontologies, as a formal representation of domain knowledge, can instruct us and ease all the tasks in building this kind of corpus. Furthermore, ontological domain knowledge leads to well-defined semantics exposed on the corpus, which will be very valuable in text mining applications.